Why Structural Firms Should Adopt an Inside-Out AI Strategy

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A few months ago, at one of the large AEC industry conferences, we met John, the IT director of a mid-sized structural engineering firm. He introduced himself as a “technology scout,” someone who hops from conference to conference in search of the latest AI tools to bring back to his organization.

When we asked him, “What’s your AI strategy?” his eyes lit up. He eagerly pulled out his laptop and walked us through dozens of presentation slides: software demos, vendor links, and proof-of-concept experiments. His enthusiasm was contagious.

But when we asked how his team was responding and how far they were in their adoption journey, his tone shifted. He sighed and said, “It’s hard. Some people are excited. Others resist. There’s a lot of pushback.”

We’ve heard this story many times, especially from engineering firms.

When we started our careers in structural engineering nearly two decades ago, technology looked very different. Fast forward to today, the landscape is flooded with more than 10,000 AI-powered tools and counting. It’s become nearly impossible to separate signal from noise.

The pace of software development has accelerated. Tools are easier to access and more abundant than ever. Amid the noise, the challenge for firms isn’t access to AI. It’s focus.

So the key question becomes:

How can structural firms adopt AI in a strategic, sustainable, and scalable way?

Let’s look at two contrasting approaches.

Many firms default to what we call the outside-in approach. Like John, they look outward, to conferences, webinars, peers, and vendors, for inspiration. They run pilots, test tools, and explore what’s trending. But when asked how these tools align with business strategy or integrate into day-to-day workflows, the answers are often vague.

This approach often results in a long list of disconnected pilots with no clear throughline. Teams experience tool fatigue. Data becomes siloed. Workflows get fragmented. And instead of excitement, internal resistance grows. Technology leaders find themselves constantly "pushing" adoption, trying to convince busy employees to engage.

While well-intentioned, the outside-in model is reactive. It chases shiny objects rather than strategic business opportunities. It focuses on toolkits but not business transformation.

A more effective and resilient model is the inside-out approach.

Here, firms begin by looking inward, identifying operational bottlenecks or opportunities, workflow inefficiencies, and unmet client needs. Only after defining these priorities, leaders and employees explore external AI tools that align with their goals.

This method is grounded, focused, and aligned with their business strategy. And most importantly, it works.

When AI is linked directly to existing pain points, adoption becomes organic. Teams don’t need to be convinced; they pull the solutions in because they see the benefits firsthand: faster site reports, automated compliance checks, optimized designs, streamlined QA/QC, and smarter documentation.

An added benefit? Strategic clarity. In a fast-moving landscape, tools will come and go. But when your AI roadmap is anchored in long-term value drivers, not vendor hype, decision-making becomes simpler and more stable.

As Jeff Bezos once said, “If you want to build a successful, sustainable business, don’t ask what could change in the next ten years. Ask what won’t.”

For structural engineering firms, what won’t change is this:

• The need to deliver more reliable, smarter structures.
• Faster project timelines.
• Lower capital and operational costs for clients.

Anchoring your AI investments in these unchanging goals helps future-proof your strategy, no matter how the tools evolve.

But strategy alone isn’t enough. Innovation without guardrails is risky, especially in structural engineering, where safety and compliance are non-negotiable.

That’s where governance plays a critical role. Governance is not about adding bureaucracy. It’s about creating safe lanes for fast-moving innovation. A strong governance framework sets clear standards for how AI is evaluated, implemented, and monitored. It minimizes AI risks, ensures data privacy, and reinforces responsible, safe usage.

So, what’s the relationship between governance and strategy? Executives must find the right balance between governance and strategy. Too loose, and you expose the firm to risk. Too strict, and you kill momentum. We saw this when ChatGPT first emerged. Some engineering firms quickly banned it over data concerns. But later, when they rolled out enterprise tools like Microsoft Copilot, they faced confusion. Employees weren’t sure what was allowed, and adoption stalled.

The goal is structured flexibility: clear policies that support experimentation, paired with education, transparency, and guidance.

A practical path forward starts with two parallel task forces:

• AI Innovation Task Force: This group identifies opportunities by mapping current bottlenecks, inefficiencies, opportunities, and client pain points. They explore how AI can solve real problems, not abstract ones. They prioritize high-impact use cases to run as pilots and build internal momentum.
• AI Governance Task Force: This team assesses risks, reviews tool terms and usage policies, and develops clear, lightweight guidelines. Their role is to ensure AI adoption is secure, compliant, and aligned with the firm's values, without stalling innovation.

Crucially, these groups must collaborate. Innovation without oversight is reckless; oversight without innovation leads to stagnation. Together, they create the balance needed for sustainable AI integration. This approach is explained in more detail in the book "Augment It," which outlines how AEC organizations can start their AI journey.

Additionally, firms should:

• Audit current workflows and tools.
• Evaluate data readiness.
• Launch basic AI literacy training.
• Establish feedback loops from pilot to scale.

Ultimately, successful AI adoption isn’t just a tech issue. It’s a leadership imperative. In the book “Disrupt It,” we explain how AEC executives can prepare their organizations for the age of AI, treating it not as a tech upgrade but as a transformational initiative that reshapes culture, business models, and operating models.

This calls for senior leaders to go beyond budget approvals. They must set the vision, champion early wins, and foster a culture of curiosity, accountability, and responsible experimentation. They must model the balance between agility and integrity.

Firms that embrace an inside-out AI strategy, supported by balanced governance, will achieve more than short-term efficiency. They’ll build resilience. They’ll deliver smarter designs, stronger performance, and deeper client value.

In an industry that’s traditionally slow to change, that’s not just a competitive edge; it’s a future-proofing strategy. ■

Visit program.acec.org/2025-joint-summer-series-artificial-intelligence to register for this summer AI webinar series.

About the Authors

Dr. Mehdi Nourbakhsh is an author, speaker, and CEO of YegaTech, a technology consulting company in the AEC industry specializing in AI strategy, governance, and implementation for AEC companies.

Dr. Sam Zolfagharian is an AI strategist, keynote speaker, and author of Disrupt It: How AEC Executives Can Transform Their Organizations in the Age of AI Disruption. As Co-Founder of YegaTech, she advises CEOs and boards on AI strategy, governance, and innovation.